HRMS-SCD:A High-Resolution Multi-Scene Satellite Imagery Dataset for Comprehensive Land-Cover Semantic Change Detection

Abstract. Semantic change detection (SCD) focuses on identifying changes in surface coverage while simultaneously classifying the types of changes. This approach provides detailed information valuable for urban planning, environmental monitoring, and other applications, making it a key area of interest in remote sensing research. Despite recent advances, existing SCD studies are hindered by the lack of high-resolution satellite imagery datasets and insufficiently comprehensive semantic label coverage in publicly available datasets. To address these limitations, we have developed a large-scale high-resolution remote sensing dataset consisting of 11,587 satellite image pairs, each with 1-meter spatial resolution and a size of 512 × 512 pixels, representing land cover changes across Beijing between 2017 and 2018. This dataset encompasses diverse land surface scenes with comprehensive semantic annotations. Furthermore, it includes full-coverage semantic segmentation labels from pre-change phases and a larger sample size of 2048 × 2048 pixels to support future research on multi-class and large-format change detection. We benchmark eight state-of-the-art SCD algorithms using this dataset, providing critical performance metrics that serve as valuable references for subsequent research. This dataset not only addresses existing gaps but also establishes a robust foundation for advancing deep learning-based semantic change detection, enabling more accurate and comprehensive analysis of complex and diverse land cover changes. More information about the project can be found at https://github.com/17x-osborn/HRMS-SCD.